A LSTM-RNN method for the lithuim-ion battery remaining useful life prediction

Prognostics and health management (PHM) can ensure that a battery system is working safely and reliably. Remaining useful life (RUL) prediction, as one main approach of PHM, provides early warning of failures that can be used to determine the necessary maintenance and replacement of batteries in advance. The existing RUL prediction techniques for lithiumion batteries are inefficient to learn the long-term dependencies of aging characteristics with the degradation evolution. This paper investigates deep-learning-enabled battery RUL prediction. The long short-term memory (LSTM) recurrent neural network (RNN) is employed to learn the capacity degradation trajectories of lithium-ion batteries. The LSTM RNN is adaptively optimized using the resilient mean square back-propagation method. The developed LSTM RNN is able to capture the underlying long-term dependencies among the degraded capacities such that an explicitly capacity-oriented RUL predictor is constructed. Experimental data from one lithium-ion battery cell is deployed for model construction and verification. This is the first known application of deep learning theory to battery RUL predictions.